报告题目：Deep Generative Estimation of Conditional Survival Function 

报 告 人：赵兴球

报告摘要：Censored Survival data are commonly encountered in modern medicine, econometrics and social science. Its unique characteristics pose significant challenges to the analysis of such data and the existing methods often suffer grave consequences when the underlying model is misspecified. To address these difficulties, we propose a model-free two-stage generative approach for estimating the conditional survival function given predictors. We first learn a conditional generator nonparametrically for the joint conditional distribution of observed variables, and then construct the nonparametric maximum likelihood estimators of conditional distribution functions based on samples from the conditional generator. Subsequently, we study the convergence properties of the proposed estimator and establish its consistency. Simulation studies under various settings show the superior performance of the deep conditional generative approach over the classical modeling approaches and applications to real data yields reasonable predictions.

报告人简介：赵兴球，香港理工大学应用数学系副系主任及人工智能数学基础研究中心副主任,教授,概率和统计学博士学位（武汉大学，McMaster大学）。研究领域包括统计机器学习，生存分析，高维数据分析，半参数和非参数方法，大偏差和中偏差理论在生存模型中的应用。她在《The Annals of Statistics》，《Journal of the American Statistical Association》等统计领域的顶级期刊上发表了许多研究论文；获得了多项香港研究资助局基金和国家自然科学基金资助；2014年和合作者获得教育部高等学校科学研究优秀成果奖自然科学二等奖。她针对区间删失数据所构建的广义对数秩检验被享有盛誉的SAS软件系统于2010年收录；担任副编辑的杂志包括：Lifetime Data Analysis (2022-), Statistics and Probability Letters (2021-), Journal of Applied Statistics (2017-)等，还担任许多统计期刊包括统计4大权威期刊的审稿人。

报告时间：2023年11月27日 10:00-12:00

联系人：李天然

报告地点：腾讯会议：831-771-125